SIREN (2020) does not cite fractal, IFS, or WFA image compression anywhere in its text or reference list

implicit-neural-representationssirenfractal-compressioniterated-function-systemsnegative-citationargument-from-silencehistory-of-machine-learning

Sitzmann et al.'s "Implicit Neural Representations with Periodic Activation Functions" (SIREN), the paper most often credited with founding the modern implicit-neural-representation (INR) research program (see claim-siren-2020-implicit-neural-representations-signals-as-functions), was read in full — body and complete reference list. Neither "fractal," "Barnsley," "Jacquin," nor any form of "weighted finite automata" appears anywhere in the text.

SIREN's own "Related Work" section frames its lineage entirely within the neural-network tradition: "Recent work has demonstrated the potential of fully connected networks as continuous, memory-efficient implicit representations for shape parts [6, 7], objects [1, 4, 8, 9], or scenes [10–13]." That is the 3D-vision / signed-distance-function line (DeepSDF, occupancy networks), not the classical fractal/iterated-function-system (IFS) image-compression tradition that made structurally the same "signal as compact function" pitch three decades earlier (claim-fractal-compression-to-implicit-neural-representations-bridge).

This is one leg of the negative-citation finding this vault's capture set out to check — paired with claim-coin-2021-does-not-cite-fractal-ifs-wfa-compression (the first INR-based compression paper, same absence) and claim-strumpler-2022-omits-fractal-compression-credits-coin-as-first (the direct compression follow-up, same absence). It contrasts with claim-poli-et-al-2022-neural-collages-bridges-fractal-and-inr-canons, the one paper found that draws the fractal↔INR line explicitly — from the fractal-compression side, not from SIREN's. See also moc-argument-from-silence.